Salinity stress in Eruca sativa (E. sativa)

#install.packages("ggpubr")
#install.packages("ggfortify")
library(ggpubr)
library(dplyr)
library(lattice)
library(survival)
library(Formula)
library(ggplot2)
library(Hmisc)
library(ggpubr)
library(tidyverse)
library(Hmisc)
library(corrplot)
library(dendextend)
library(ggfortify)

Introduction

Under abiotic stress such as salinity, several morphological and physiological parameters of plants are affected. In function of their location, plants from the same species could exhibit difference tolerance to abiotic stress. In the study we will review, 25 accessions of E. sativa from different regions of Pakistan were analysed. Each accession have been separated into two groups receiving different treatment: first group is the control and the second underwent salt stress (150 mM NaCl) during two weeks. Following that 19 morpho-physiological traits were measured.

The data was obtained from the paper "Evaluation of salt tolerance in Eruca sativa accessions based on morpho-physiological traits" (Afsar et. al 2020) https://peerj.com/articles/9749/

Objectives and hypotheses

Our main objectives are:

1. Explore the effects of salt stress on various morpho-physiological traits in different E. sativa accessions.
2. Define the susceptible and resistant accessions
3. Identify traits the salt stress has the biggest influence on
4. Compare our results with the ones from the paper

Main hypothesis:

• Susceptible accessions have statistically different morpho-physiological traits compared to their control.
• The susceptible accessions have more morpho-physiological traits affected

Methods

What have you done? And what did you use to do it? Step by step, so anyone could do it again by reading this part.

First we had to download and tidy the data. Once we have the table containing the mean values of variables for each accessions and treatments, we tested the normality of variables with shapiro.test. Then, we analysed if there is a significant difference between salt-treated and control plants for each variable. If there was normality, the analysis was performed with anova (only one variable), otherwise we used kruskal-Wallis test.

Additionaly, we did a summary table for the mean and sd of treated and control plant.

We created another table with data converted into salt tolerance index (STI): ratio of treated plant values to control.

Then, we computed the MVF table using the formula provided in the article: \(Xp=[(X−Xmin)/(Xmax−Xmin)]×100%\). For the traits inversely related to salt tolerance (Electrolyte leakage, Na, K, Ca) we used the second formula: \(Xp=[1−(X−Xmin)/(Xmax−Xmin)]×100%\) For calculating MFV, we used the same formula, however we used the min and max of the mean STI.

We performed the Principal Component Analysis on the MFV values of all the variables, except for RWC and ICO2 because they did not show significant difference between treatment and control. We used 8 highest ranked variables from the results: PH, SL, RL, NL, DW, FW, LA, K_Na.

We computed the mean of MFV from MFV values of the variables above. With the results, we ranked the accessions from highly tolerant to highly sensitive.

We started pearson correlation but we still need to finish it. We started doing peason correlation using MFV values which results are very similar to the article. But we also computed on the raw data because we think it has more sence.

We still need to make the dendogram, add map with accession and check for correlation between altitude and MFV ranking.

Results

(Figures and tables with captions and description of what do they mean.)

(1) Tidying of data

##Calling for the libraries to transform and tidy the data
library(dplyr)
library(tidyr)
##Downloading the data and transforming it 
#install.packages("ggplot2", dependencies = TRUE)
library(ggplot2)
#install.packages("readxl")
library(readxl)
# downloading excel sheet from internet and storing it into Excel object
Excel <- "https://dfzljdn9uc3pi.cloudfront.net/2020/9749/1/Raw_data_Afsar_et_al.%2C_2020-PeerJ_20.5.2020.xlsx"
# creating the folder datafile if it doesn't exist
D <- "data"
if (!dir.exists(D)) dir.create(D)
f <- paste0 (D, "/data.xlsx")
# downloading excel into the file datafile under the name data.xlsx
download.file(Excel, f, mode="wb")
# getting the name of excel sheets
excel.sheet <- excel_sheets(f)

# created a list with the 4 sheet table from excel 
X <- list()
for (i in excel.sheet){
  X[[i]] <- as.data.frame(read_xlsx(f,i))
}

# organise column names and fill the first 2 columns
for(i in names(X)){
  names(X[[i]])[1:3] <- c("Number", "Code", "Treat_Contr")
  X[[i]] <- X[[i]][-1,]
  #X[[i]] <- X[[i]][,-1] - deletion of numbers
  X[[i]] <- fill(X[[i]], 1:2)
}

#deleting the accession #7 and #16 from Morphological traits,
#Weight_ion, Chloro_c, because there is no data for that accessions in the Gas_e
#for(i in names(X)){
#  X[[i]] <- X[[i]][!X[[i]]$Code == "Es-7", ]
#  X[[i]] <- X[[i]][!X[[i]]$Code == "Es-16", ]
#}

#transforming columns data format 
for (i in seq_along(X)){
  X[[i]][2:3] <- lapply(X[[i]][2:3], as.factor)
  X[[i]][-(2:3)] <- lapply(X[[i]][-(2:3)], as.numeric)
}

# creating data frames from different xlsx sheets
Morpho_t <- X$`Morphological traits`
Weight_ion <- X$`FW DW RWC Ions EL`
Chloro_c <- X$`Chlorophyll content`
Gas_e <- X$`Gas Exchange parameters`

#Changing names of the columns for more convinient use
names(Morpho_t)[4:8] <- c('Shoot_Length', 
                          'Root_Length', 
                          'Plant_Height', 
                          'Number_Leaves', 
                          'Leaf_Area')
names(Chloro_c)[4] <- c('Chlorophyll_Content')
names(Weight_ion) [4:12] <- c('Fresh_Weight', 
                              'Dry_Weight', 
                              'RWC',
                              'Na',
                              'K', 
                              'Ca', 
                              'Mg', 
                              'K_Na', 
                              'Electrolyte_Leakage')
names(Gas_e) [4:7] <- c('Photsynthesis_Rate', 
                        'Intercellular_CO2', 
                        'Transpiration_Rate', 
                        'Stomatal_Conductance')

# adding NA cells into the missing 7th and 16th accession
# creating a data frame with the missing data first
Gas_e_miss <- data.frame (Number = rep(c(7, 16), each = 8),
                          Code = rep(c("Es-7", "Es-16"), each = 8),
                          Treat_Contr = rep(c("Treatment", "Control",
                                            "Treatment", "Control"), each = 4),
                          Photsynthesis_Rate = rep(NA, 16),
                          Intercellular_CO2 = rep(NA, 16),
                          Transpiration_Rate = rep(NA, 16),
                          Stomatal_Conductance = rep(NA, 16))
#changing the numbers in "Number" column
Gas_e$Number <- replace(Gas_e$Number, , rep(c(1:6, 8:15, 17:25), each = 8))
# adding NA cells into the missing 7th and 16th accession
if (!(is.element('Es-7', Gas_e$Code))) Gas_e <- rbind(Gas_e, Gas_e_miss)
#Sorting the variables according to Number
Gas_e <- Gas_e[order(Gas_e$Number),]
#Rewriting row names to correspond the ordered data
row.names(Gas_e) <- c(1:200)

# downloading data for the location of accessions
word <- "https://dfzljdn9uc3pi.cloudfront.net/2020/9749/1/Table_S1.docx"
w <- paste0(D, "/location.docx")
download.file(word, w, mode="wb")

#creating data frame for the location of accessions
#install.packages("docxtractr")
library(docxtractr)
Acc_loc <- docx_extract_all_tbls(read_docx(w, track_changes = NULL),
                                 guess_header = TRUE, preserve = FALSE,
                                 trim = TRUE)
Acc_loc <- as.data.frame(Acc_loc)
#removing m from Elevation column 
Acc_loc$Elevation <- sub(' m', '', Acc_loc$Elevation)
#transforming data in the accession_loc
Acc_loc[c(1,6)] <- lapply(Acc_loc[c(1,6)], as.numeric)
Acc_loc[-c(1,6)] <- lapply(Acc_loc[-c(1,6)], as.factor)
#In order to fit the length of this table to the length of the ultimate table
#we need to double all the rows
Acc_loc_double <- Acc_loc[rep(1:25, each = 2), ]

###
#experimenting with mapping the data
#map <- data.frame(Code = Acc_loc$Code,
#                  Latitude = Acc_loc$Latitude,
#                  Longitude = Acc_loc$Longitude)
#m <- ggplot(map, aes(x = Latitude,
#                     y = Longitude))
#m +
#  geom_jitter() +
#  geom_label(aes(label = Code))


# create summary table for morphological trait
a <- Morpho_t %>% 
  group_by(Number, Code, Treat_Contr) %>% 
  summarise(Shoot_Length = mean(Shoot_Length),
            Root_Length = mean(Root_Length), 
            Plant_Height = mean(Plant_Height), 
            Number_Leaves = mean(Number_Leaves), 
            Leaf_Area = mean(Leaf_Area))

# remove NA values from Weight_ion table
Weight_ion <- na.omit(Weight_ion)
#Create summary table for Weight ion
b <- Weight_ion %>% 
  group_by(Number, Code, Treat_Contr) %>% 
  summarise(Fresh_Weight= mean(Fresh_Weight),
            Dry_Weight= mean(Dry_Weight),
            RWC = mean(RWC),
            Na =mean(Na),
            K =mean(K),
            Ca =mean(Ca),
            Mg= mean(Mg),
            K_Na =mean(K_Na),
            Electrolyte_Leakage= mean(Electrolyte_Leakage))
#take only the columns number 4 to 12 to avoid repetition of number, code and treatment
b <- b[4:12] 

#Create summary table for Weight_ion
c <- Chloro_c %>% 
  group_by(Number, Code, Treat_Contr) %>% 
  summarise(Chlorophyll_Content = mean(Chlorophyll_Content))
#take only the columns number 4 to avoid repetition of number, code and treatment
c <- c[4]  

#Create summary table for Gas_e
d <- Gas_e %>% 
  group_by(Number, Code, Treat_Contr) %>% 
  summarise(Photsynthesis_Rate= mean(Photsynthesis_Rate),
            Intercellular_CO2= mean(Intercellular_CO2), 
            Transpiration_Rate= mean(Transpiration_Rate), 
            Stomatal_Conductance=mean(Stomatal_Conductance))
#take only the columns number 4 to 7 to avoid repetition of number, code and treatment
d <- d[4:7]

#add Acc_loc data to the table
e <- Acc_loc_double
e <- e[6]

#create data frame with all the summarized tables 
table <- data.frame(a, b, c, d, e)

knitr::kable(table, caption = "Table 1. Mean values of variables for each accessions and treatments", align="c" , digits = round(2))

Table 1. Mean values of variables for each accessions and treatments

	Number	Code	Treat_Contr	Shoot_Length	Root_Length	Plant_Height	Number_Leaves	Leaf_Area	Fresh_Weight	Dry_Weight	RWC	Na	K	Ca	Mg	K_Na	Electrolyte_Leakage	Chlorophyll_Content	Photsynthesis_Rate	Intercellular_CO2	Transpiration_Rate	Stomatal_Conductance	Elevation
1	1	Es-1	Control	1.89	2.40	4.16	4.20	0.05	343.10	20.93	75.84	4.60	8.72	4.02	2.74	1.90	51.07	16.88	12.41	286.0	3.16	0.22	14
1.1	1	Es-1	Treatment	1.69	1.54	3.08	2.60	0.03	171.97	18.06	89.81	21.50	11.55	3.51	2.54	0.54	48.42	13.65	4.62	245.5	1.64	0.08	14
2	2	Es-2	Control	2.83	1.93	4.99	4.40	0.08	173.13	11.97	71.60	6.04	8.81	4.28	3.49	1.46	61.86	21.17	2.74	271.5	0.90	0.04	17
2.1	2	Es-2	Treatment	1.54	1.27	2.53	2.80	0.04	58.84	9.00	91.74	23.86	12.99	3.74	2.83	0.55	62.49	15.20	1.78	288.5	1.12	0.04	17
3	3	Es-3	Control	3.51	2.35	4.62	4.40	0.16	321.27	15.93	76.90	7.44	9.59	5.69	3.79	1.31	64.60	18.67	17.00	290.5	4.18	0.31	579
3.1	3	Es-3	Treatment	0.69	0.45	1.19	1.33	0.01	32.76	3.93	89.22	17.00	14.46	5.74	3.55	0.85	78.67	13.20	1.34	258.0	0.56	0.02	579
4	4	Es-4	Control	3.56	2.75	6.05	4.60	0.10	347.55	19.72	96.03	7.08	6.94	2.69	5.11	0.98	60.97	16.90	13.00	279.0	3.55	0.21	537
4.1	4	Es-4	Treatment	2.07	1.98	3.83	3.80	0.04	165.95	12.45	96.34	22.16	9.74	2.92	3.88	0.44	65.64	15.88	3.40	273.0	1.38	0.06	537
5	5	Es-5	Control	2.48	1.65	4.51	4.40	0.07	222.80	12.93	91.39	9.97	12.06	5.29	7.24	1.21	49.65	23.72	9.11	280.0	3.45	0.20	523
5.1	5	Es-5	Treatment	1.08	0.97	2.09	2.00	0.06	170.20	10.83	94.64	27.53	13.02	4.01	4.89	0.47	65.76	16.82	2.62	312.5	1.94	0.05	523
6	6	Es-6	Control	2.01	1.48	3.55	4.60	0.08	753.50	32.50	98.75	10.69	12.13	3.12	5.12	1.14	59.58	17.08	4.96	281.5	3.88	0.20	186
6.1	6	Es-6	Treatment	1.23	1.04	2.18	3.80	0.03	235.50	10.32	95.90	25.12	14.73	2.99	3.84	0.59	58.45	12.58	1.22	315.0	1.03	0.03	186
7	7	Es-7	Control	1.95	2.19	4.08	4.00	0.08	323.13	10.60	97.49	13.15	16.65	3.98	3.69	1.27	46.84	26.30	NA	NA	NA	NA	159
7.1	7	Es-7	Treatment	1.49	1.15	2.69	2.40	0.06	96.48	8.52	96.00	30.73	16.97	3.04	2.71	0.55	50.04	31.32	NA	NA	NA	NA	159
8	8	Es-8	Control	2.97	1.80	4.66	5.00	0.07	245.13	11.20	94.58	15.59	17.60	7.78	9.49	1.13	69.75	9.80	2.86	300.5	1.57	0.07	97
8.1	8	Es-8	Treatment	0.92	1.05	1.98	2.40	0.04	76.17	5.59	92.02	31.90	21.97	6.39	8.85	0.69	83.50	6.93	1.54	333.0	1.30	0.05	97
9	9	Es-9	Control	2.42	2.23	4.65	4.00	0.07	60.59	3.48	91.56	16.21	33.40	8.99	8.11	2.07	50.83	27.40	14.01	255.0	3.46	0.23	355
9.1	9	Es-9	Treatment	1.71	1.39	3.10	3.00	0.04	50.85	2.72	90.26	31.85	41.31	13.46	5.33	1.30	56.62	31.32	2.46	251.0	0.68	0.03	355
10	10	Es-10	Control	1.38	1.61	3.04	5.00	0.08	435.07	16.00	96.41	9.82	12.51	4.69	3.44	1.30	55.62	18.52	8.80	281.5	3.48	0.17	840
10.1	10	Es-10	Treatment	1.06	0.75	1.89	2.20	0.07	177.52	7.18	94.73	35.98	16.71	8.52	2.91	0.46	56.00	13.00	2.51	258.5	1.23	0.06	840
11	11	Es-11	Control	2.06	1.89	3.99	4.40	0.06	205.72	7.28	94.66	15.82	18.40	9.79	4.41	1.17	54.23	14.32	3.29	290.0	1.65	0.07	355
11.1	11	Es-11	Treatment	1.27	1.13	2.33	3.60	0.05	144.61	6.43	95.90	18.54	20.71	11.00	4.25	1.12	54.21	9.35	1.16	329.0	0.63	0.03	355
12	12	Es-12	Control	1.78	1.66	3.44	4.40	0.06	247.10	10.80	94.75	16.64	14.60	4.02	6.07	0.88	57.91	15.67	9.07	255.5	3.14	0.15	178
12.1	12	Es-12	Treatment	1.37	1.22	2.36	3.00	0.02	193.27	9.27	95.20	20.31	11.98	7.14	5.51	0.59	59.02	14.37	3.31	281.5	1.84	0.06	178
13	13	Es-13	Control	2.73	2.50	5.34	5.00	0.07	449.30	10.90	96.10	10.56	15.25	5.45	4.46	1.44	47.15	15.00	14.80	294.0	3.64	0.20	140
13.1	13	Es-13	Treatment	1.97	1.44	3.41	4.00	0.04	176.71	9.17	94.59	27.91	15.77	8.00	3.15	0.57	47.84	9.08	2.50	223.5	0.68	0.03	140
14	14	Es-14	Control	2.29	2.41	4.79	5.60	0.08	426.00	14.00	97.35	12.85	13.03	4.43	4.19	1.02	49.72	24.88	8.83	294.5	2.58	0.20	190
14.1	14	Es-14	Treatment	1.23	1.49	2.80	3.20	0.03	165.56	8.61	94.23	35.57	18.91	10.12	3.73	0.53	55.17	17.52	3.18	274.5	1.25	0.05	190
15	15	Es-15	Control	2.07	2.42	4.38	4.75	0.05	250.90	11.80	95.26	15.84	12.65	4.25	4.53	0.80	59.46	16.55	5.16	315.5	1.93	0.10	169
15.1	15	Es-15	Treatment	0.35	0.97	1.64	1.60	0.01	36.60	3.60	91.67	21.56	16.92	5.76	3.12	0.78	62.24	9.63	1.37	215.5	0.26	0.01	169
16	16	Es-16	Control	2.42	2.31	4.65	4.60	0.07	77.00	3.67	90.82	10.68	13.63	5.49	3.87	1.30	49.17	21.22	NA	NA	NA	NA	268
16.1	16	Es-16	Treatment	0.69	0.45	1.23	1.33	0.01	33.03	3.01	88.90	22.88	19.37	12.14	2.83	0.84	52.06	12.67	NA	NA	NA	NA	268
17	17	Es-17	Control	2.44	2.23	4.69	5.00	0.08	210.20	10.40	94.25	17.88	17.13	7.26	6.26	0.96	60.17	15.18	1.14	340.0	1.06	0.05	207
17.1	17	Es-17	Treatment	0.60	0.45	1.23	1.40	0.02	36.28	3.00	90.90	47.06	26.70	22.98	4.59	0.58	63.78	9.22	0.76	315.0	0.37	0.03	207
18	18	Es-18	Control	3.05	2.90	5.87	5.20	0.07	307.93	8.67	96.56	24.72	22.81	4.89	5.05	0.93	62.19	9.62	2.84	306.0	1.40	0.07	162
18.1	18	Es-18	Treatment	0.60	0.46	1.16	2.00	0.01	41.04	2.80	88.61	38.07	25.59	13.62	3.84	0.68	68.11	12.50	1.43	349.5	1.00	0.03	162
19	19	Es-19	Control	2.25	2.18	4.43	3.40	0.04	104.92	5.34	92.96	12.51	18.37	2.61	5.99	1.47	57.77	18.75	2.16	287.0	3.64	0.20	186
19.1	19	Es-19	Treatment	1.23	1.49	2.80	3.20	0.03	70.57	4.08	94.69	32.73	22.20	6.84	3.78	0.68	59.43	16.52	1.62	293.0	0.56	0.04	186
20	20	Es-20	Control	1.99	2.34	4.33	3.00	0.04	42.25	3.10	88.38	13.68	26.53	13.48	6.81	1.94	62.26	22.22	12.70	238.0	3.00	0.16	186
20.1	20	Es-20	Treatment	0.35	0.97	2.80	1.60	0.01	30.15	2.60	91.38	48.28	33.45	21.62	4.74	0.72	65.09	14.67	1.75	215.5	0.46	0.02	186
21	21	Es-21	Control	2.03	2.68	4.72	3.20	0.04	90.56	4.18	94.99	24.90	24.81	8.28	10.54	1.00	46.49	9.95	12.18	274.0	4.14	0.25	171
21.1	21	Es-21	Treatment	0.60	0.46	1.78	2.00	0.02	61.55	3.25	95.42	44.92	30.38	12.05	5.82	0.68	48.78	16.90	2.58	265.5	1.23	0.06	171
22	22	Es-22	Control	1.99	2.47	4.33	3.40	0.04	48.30	2.02	96.41	16.72	12.71	9.38	5.53	0.76	59.96	18.98	4.26	259.0	1.32	0.06	170
22.1	22	Es-22	Treatment	1.22	0.78	2.13	2.00	0.04	35.92	1.32	95.93	28.21	15.63	13.33	4.66	0.56	71.26	8.02	1.24	275.0	0.82	0.03	170
23	23	Es-23	Control	1.73	1.80	3.53	3.00	0.05	138.11	4.17	94.44	17.41	10.94	8.64	3.17	0.63	63.71	21.50	5.04	246.0	1.56	0.06	210
23.1	23	Es-23	Treatment	0.63	0.53	1.17	2.00	0.03	37.66	3.38	90.88	26.89	16.58	9.95	2.30	0.62	66.40	8.83	0.78	264.0	0.66	0.03	210
24	24	Es-24	Control	1.40	1.41	2.84	3.00	0.04	107.80	7.67	90.98	13.83	13.91	6.39	14.25	1.01	61.26	14.22	4.47	276.5	1.52	0.06	209
24.1	24	Es-24	Treatment	0.75	0.49	1.26	1.60	0.03	66.41	3.15	89.62	28.33	19.10	7.40	5.60	0.68	67.51	9.42	0.90	361.0	0.61	0.03	209
25	25	Es-25	Control	2.39	2.74	5.18	3.00	0.04	95.34	5.29	93.99	15.45	13.94	5.81	11.36	0.91	50.58	19.83	2.27	266.0	0.99	0.06	151
25.1	25	Es-25	Treatment	1.17	0.64	1.84	2.00	0.04	39.93	4.31	90.57	41.97	26.23	11.76	9.95	0.62	48.61	12.95	0.71	350.5	0.38	0.03	151

(2) Statistical analysis on variables

i. Morphological traits

There is a statistical difference between control and treatment for all morphological trait

• Shoot length (SL): Shoot length is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for SL in salt-stressed plants is 1.10 ± 0.48 and 2.30 ± 0.56 for control plants. SL stress index of accessions varied from 16.73 to 89.12

• Root length (RL): Root length is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for RL in salt-stressed plants is 0.98 ± 0.43 and 2.17 ± 0.42 for control plants. RL stress index of accessions varied from 15.91 to 73.53

• Plant height (PH): Plant height is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for PH in salt-stressed plants is 2.18 ± 0.76 and 4.43 ± 0.77 for control plants. PH stress index of accessions varied from 19.83 to 74.07

• Number of leaves(NL): Number of leaves is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for NL in salt-stressed plants is 2.43 ± 0.82 and 4.22 ± 0.78 for control plants. NL stress index of accessions varied from 28.00 to 94.12

• Leaf area (LA): Leaf area is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for LA in salt-stressed plants is 0.03 ± 0.02 and 0.07 ± 0.03 for control plants. LA stress index of accessions varied from 75.95 to 100

spm <-  numeric()
nor_spm <- c()
for(i in names(Morpho_t[4:8])) { 
  an1 <- aov(Morpho_t[, i] ~ Morpho_t$Treat_Contr)
  spm[i] <- shapiro.test(an1$residuals)$p.value
  nor_spm <- c(nor_spm, ifelse (spm[i] <= 0.05, "False", "True"))
}
spm_table <- data.frame ("P-value" = spm, "Normality" = nor_spm)

knitr::kable(spm_table, caption = "Table 2. Shapiro test results for morphological traits", align="c")

Table 2. Shapiro test results for morphological traits

	P.value	Normality
Shoot_Length	0.0000307	False
Root_Length	0.0187623	False
Plant_Height	0.0315918	False
Number_Leaves	0.0000000	False
Leaf_Area	0.0000000	False

#install.packages("ggpubr")
library(ggpubr)

for (i in names(Morpho_t[4:8])) {
  bmorpho <- ggboxplot(Morpho_t, 
                 x = "Treat_Contr",
                 y = names(Morpho_t[i]), 
                 color = names(Morpho_t[3]),
                 palette = c("blue", "red"),
                 add = "jitter") + 
    stat_compare_means(data = Morpho_t, method = "kruskal.test", label.x.npc = "center",
                       label.y.npc = "top")
  print(bmorpho)
} 

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ii) Biomass

• Fresh weight (FW): Fresh weight is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for FW in salt-stressed plants 96.22 ± 66.00 and 241.06 ± 164.75 for control plants. FW stress index of accessions varied from 10.20 to 83.93

• Dry weight (DW): Dry weight is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for DW in salt-stressed plants is 6.26 ± 4.00 and 10.58 ± 6.89 for control plants. DW stress index of accessions varied from 24.67 to 88.26

spb <- numeric()
nor_spb <- c()
for(i in names(Weight_ion[4:5])) { 
  an2 <- aov(Weight_ion[, i] ~ Weight_ion$Treat_Contr)
  spb[i] <- shapiro.test(an2$residuals)$p.value
  nor_spb <- c(nor_spb, ifelse (spb[i] <= 0.05, "False", "True"))
}
spb_table <- data.frame ("P-value" = spb, "Normality" = nor_spb)



knitr::kable(spb_table, caption = "Table 3. Shapiro test results for biomass", align="c")

Table 3. Shapiro test results for biomass

	P.value	Normality
Fresh_Weight	3e-07	False
Dry_Weight	7e-07	False

for (i in names(Weight_ion[c(6,12)])) {
  bwei2 <- ggboxplot(Weight_ion, 
                 x = "Treat_Contr",
                 y = names(Weight_ion[i]), 
                 color = names(Weight_ion[3]),
                 palette = c("blue", "red"),
                 add = "jitter") + 
    stat_compare_means(data = Weight_ion, method = "kruskal.test", label.x.npc = "center",
                       label.y.npc = "top")
  print(bwei2)
} 

iii) RWC, EL and chlorophyll content

• Relative water content (RWC): Relative water content is not significantly different in the salt-stressed plants compared with the control (p > 0.05). The mean value for RWC in salt-stressed plants 92.77 X ± 2.65 and 92.10 ± 7.00 for control plants.

• Electrolyte leakage (EL): Electrolyte leakage is significantly increased in the salt-stressed plants compared with the control (p < 0.05). The mean value for EL in salt-stressed plants is 60.60 ± 9.32 and 56.51 ± 6.48 for control plants.

• Chlorophyll content (CC): Chlorophyll content is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for CC in salt-stressed plants is 14.06 ± 6.02 and 18.17 ± 4.79 for control plants.

spe <- numeric()
nor_spe <- c()
for(i in names(Weight_ion[c(6,12)])) { 
  an3 <- aov(Weight_ion[, i] ~ Weight_ion$Treat_Contr)
  spe[i] <- shapiro.test(an3$residuals)$p.value
  nor_spe <- c(nor_spe, ifelse (spe[i] <= 0.05, "False", "True"))
}
spe_table <- data.frame ("P-value" = spe, "Normality" = nor_spe)

nor_spc <- c()
an4 <- aov(Chloro_c$Chlorophyll_Content ~ Chloro_c$Treat_Contr)
spc <- shapiro.test(an4$residuals)$p.value
nor_spc <- c(nor_spc, ifelse (spc <= 0.05, "False", "True"))

spc_table <- data.frame ("P-value" = spc, "Normality" = nor_spc)
row.names(spc_table) <- "Chlorophyll_Content"

spec_table <- dplyr::bind_rows(spe_table, spc_table)


knitr::kable(spec_table, caption = "Table 4. Shapiro test results for RWC, EL and chlorophyll content", align="c")

Table 4. Shapiro test results for RWC, EL and chlorophyll content

	P.value	Normality
RWC	0.0000000	False
Electrolyte_Leakage	0.0009774	False
Chlorophyll_Content	0.0000000	False

for (i in names(Weight_ion[7:11])) {
  bwei3 <- ggboxplot(Weight_ion, 
                 x = "Treat_Contr",
                 y = names(Weight_ion[i]), 
                 color = names(Weight_ion[3]),
                 palette = c("blue", "red"),
                 add = "jitter") + 
    stat_compare_means(data = Weight_ion, method = "kruskal.test", label.x.npc = "center",
                       label.y.npc = "top")
  print(bwei3)
} 

bchlo <- ggboxplot(Chloro_c, 
                 x = "Treat_Contr",
                 y = "Chlorophyll_Content", 
                 color = names(Chloro_c[3]),
                 palette = c("blue", "red"),
                 add = "jitter") 
bchlo + stat_compare_means(data = Chloro_c, method = "kruskal.test", label.x.npc = "center",
                       label.y.npc = "top")

iv) Mineral ion content

• Sodium (Na): Sodium is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for Na in salt-stressed plants is 30.03 ± 8.84 and 13.60 ± 5.00 for control plants.

• Potassium (K): Potassium is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for K in salt-stressed plants is 19.72 ± 7.48 and 15.48 ± 6.09 for control plants.

• Calcium (Ca): Calcium is significantly increased in the salt-stressed plants compared with the control (p < 0.05). The mean value for Ca in salt-stressed plants is 9.12 ± 5.32 and 6.03 ± 2.60 for control plants.

• Magnesium (Mg): Magnesium is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for Mg in salt-stressed plants is 4.37 ± 1.83 and 5.95 ± 2.85 for control plants.

• K/Na ratio: K/Na ratio is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for K/Na ratio in salt-stressed plants is 0.67 ± 0.20 and 1.20 ± 0.36 for control plants.

spw <- numeric()
nor_spw <- c()
for(i in names(Weight_ion[7:11])) { 
  an5 <- aov(Weight_ion[, i] ~ Weight_ion$Treat_Contr)
  spw[i] <- shapiro.test(an5$residuals)$p.value
  nor_spw <- c(nor_spw, ifelse (spw[i] <= 0.05, "False", "True"))
}
spw_table <- data.frame ("P-value" = spw, "Normality" = nor_spw)

knitr::kable(spw_table, caption = "Table 5. Shapiro test results for Mineral ion content", align="c")

Table 5. Shapiro test results for Mineral ion content

	P.value	Normality
Na	0.0002216	False
K	0.0000000	False
Ca	0.0000007	False
Mg	0.0000000	False
K_Na	0.0000002	False

for (i in names(Weight_ion[7:11])) {
  bwei3 <- ggboxplot(Weight_ion, 
                 x = "Treat_Contr",
                 y = names(Weight_ion[i]), 
                 color = names(Weight_ion[3]),
                 palette = c("blue", "red"),
                 add = "jitter") + 
    stat_compare_means(data = Weight_ion, method = "kruskal.test", label.x.npc = "center",
                       label.y.npc = "top")
  print(bwei3)
} 

v) Gas exchange attributes

• Photosynthesis rate (PR): Photosynthesus rate is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for PR in salt-stressed plants is 1.95 ± 1.02 and 7.53 ± 4.84 for control plants.

• Intercellular CO2 (ICO2): Intercellular CO2 is not significantly different in the salt-stressed plants compared with the control (p < 0.05). The mean value for ICO2 in salt-stressed plants is 284.70 ± 42.57 and 281.20 ± 22.90 for control plants.

• Transpiration rate (TR): Transpiration rate is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for TR in salt-stressed plants is 0.94 ± 0.48 and 2.57 ± 1.13 for control plants.

• Stomatal conductance (SC): Stomatal conductance is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for SC in salt-stressed plants is 0.039 ± 0.02 and 0.15 ± 0.08 for control plants.

spg <- numeric()
nor_spg <- c()
for (i in names(Gas_e[c(4,5,6:7)])) { 
  an6 <- aov(Gas_e[, i] ~ Gas_e$Treat_Contr)
  spg[i] <- shapiro.test(an6$residuals)$p.value
  nor_spg <- c(nor_spg, ifelse (spg[i] <= 0.05, "False", "True"))
}

spg_table <- data.frame ("P-value" = spg, "Normality" = nor_spg)

knitr::kable(spg_table, caption = "Table 6. Shapiro test results for gas exchange attributes", align="c")

Table 6. Shapiro test results for gas exchange attributes

	P.value	Normality
Photsynthesis_Rate	0.0000139	False
Intercellular_CO2	0.8962970	True
Transpiration_Rate	0.0384559	False
Stomatal_Conductance	0.0000015	False

Gas_e_withoutNA <- na.omit(Gas_e)
for (i in names(Gas_e_withoutNA[c(4,6:7)])) {
  bgas <- ggboxplot(Gas_e_withoutNA, 
                 x = "Treat_Contr",
                 y = names(Gas_e_withoutNA[i]), 
                 color = names(Gas_e_withoutNA[3]),
                 palette = c("blue", "red"),
                 add = "jitter") + 
    stat_compare_means(data = Gas_e_withoutNA, method = "kruskal.test", label.x.npc = "center",label.y.npc = "top")
print(bgas)} 

banova <- ggboxplot(Gas_e_withoutNA,
          x="Treat_Contr", 
          y= "Intercellular_CO2",
          color = names(Gas_e_withoutNA[3]),
          palette = c("blue", "red"),
          add = "jitter") 
banova  +   stat_compare_means(method = "anova")  

x <- data.frame(dplyr::filter(table, table$Treat_Contr=="Treatment"))
x <- x[-(1:3)]
y <- data.frame(dplyr::filter(table, table$Treat_Contr=="Control"))
y <- y[-(1:3)]

Mean_Treat <- x %>% 
  summarise(Mean_SL = mean(Shoot_Length),
            Mean_SL = mean(Root_Length), 
            Mean_PH = mean(Plant_Height), 
            Mean_NL = mean(Number_Leaves), 
            Mean_LA = mean(Leaf_Area),
            Mean_FW = mean(Fresh_Weight),
            Mean_DW = mean(Dry_Weight),
            Mean_RWC = mean(RWC),
            Mean_Na = mean(Na),
            Mean_K = mean(K),
            Mean_Ca = mean(Ca),
            Mean_Mg = mean(Mg),
            Mean_K_Na = mean(K_Na),
            Mean_EL = mean(Electrolyte_Leakage),
            Mean_CC = mean(Chlorophyll_Content),
            Mean_PR = mean (Photsynthesis_Rate, na.rm=T),
            Mean_ICO2 = mean(Intercellular_CO2, na.rm=T),
            Mean_TR = mean(Transpiration_Rate, na.rm=T),
            Mean_S = mean(Stomatal_Conductance, na.rm=T)) 
Mean_Control <- y %>% 
  summarise(Mean_SL = mean(Shoot_Length),
            Mean_SL = mean(Root_Length), 
            Mean_PH = mean(Plant_Height), 
            Mean_NL = mean(Number_Leaves), 
            Mean_LA = mean(Leaf_Area),
            Mean_FW = mean(Fresh_Weight),
            Mean_DW = mean(Dry_Weight),
            Mean_RWC = mean(RWC),
            Mean_Na = mean(Na),
            Mean_K = mean(K),
            Mean_Ca = mean(Ca),
            Mean_Mg = mean(Mg),
            Mean_K_Na = mean(K_Na),
            Mean_EL = mean(Electrolyte_Leakage),
            Mean_CC = mean(Chlorophyll_Content),
            Mean_PR = mean (Photsynthesis_Rate, na.rm=T),
            Mean_ICO2 = mean(Intercellular_CO2, na.rm=T),
            Mean_TR = mean(Transpiration_Rate, na.rm=T),
            Mean_S = mean(Stomatal_Conductance, na.rm=T)) 
Treatment <- c('Treatment','Control')
Means <- rbind(Mean_Treat, Mean_Control)
Means$Treat_Contr <- Treatment

Sd_Treatment <- x %>%
    summarise(SD_SL = sd(Shoot_Length),
            SD_RL = sd(Root_Length),
            SD_PH = sd(Plant_Height),
            SD_NL = sd(Number_Leaves),
            SD_LA = sd(Leaf_Area),
            SD_FW = sd(Fresh_Weight),
            SD_DW = sd(Dry_Weight),
            SD_RWC = sd(RWC),
            SD_Na = sd(Na),
            SD_K = sd(K),
            SD_Ca = sd(Ca),
            SD_Mg = sd(Mg),
            SD_K_NA = sd(K_Na),
            SD_EL = sd(Electrolyte_Leakage),
            SD_CC = sd(Chlorophyll_Content),
            SD_PR = sd (Photsynthesis_Rate, na.rm=T),
            SD_I_CO2 = sd(Intercellular_CO2, na.rm=T),
            SD_TR = sd(Transpiration_Rate, na.rm=T),
            SD_SC = sd(Stomatal_Conductance, na.rm=T))
Sd_Control <-   y %>%
  summarise(SD_SL = sd(Shoot_Length),
            SD_RL = sd(Root_Length),
            SD_PH = sd(Plant_Height),
            SD_NL = sd(Number_Leaves),
            SD_LA = sd(Leaf_Area),
            SD_FW = sd(Fresh_Weight),
            SD_DW = sd(Dry_Weight),
            SD_RWC = sd(RWC),
            SD_Na = sd(Na),
            SD_K = sd(K),
            SD_Ca = sd(Ca),
            SD_Mg = sd(Mg),
            SD_K_NA = sd(K_Na),
            SD_EL = sd(Electrolyte_Leakage),
            SD_CC = sd(Chlorophyll_Content),
            SD_PR = sd (Photsynthesis_Rate, na.rm=T),
            SD_I_CO2 = sd(Intercellular_CO2, na.rm=T),
            SD_TR = sd(Transpiration_Rate, na.rm=T),
            SD_SC = sd(Stomatal_Conductance, na.rm=T))
Treatment <- c('Treatment','Control')
SD <- rbind(Sd_Treatment,Sd_Control)
SD$Treat_Contr <- Treatment

knitr::kable(Means, caption = "Table 7. Summary table for the mean of treated and control plants")

Table 7. Summary table for the mean of treated and control plants

Mean_SL	Mean_PH	Mean_NL	Mean_LA	Mean_FW	Mean_DW	Mean_RWC	Mean_Na	Mean_K	Mean_Ca	Mean_Mg	Mean_K_Na	Mean_EL	Mean_CC	Mean_PR	Mean_ICO2	Mean_TR	Mean_S	Treat_Contr
0.98288	2.17992	2.43456	0.03304	96.22133	6.262533	92.76587	30.03500	19.71913	9.120667	4.368333	0.6673333	60.60373	14.06147	1.947391	284.6957	0.9404348	0.0386957	Treatment
2.17392	4.43336	4.22200	0.06664	241.06840	10.582267	92.09667	13.60367	15.48453	6.027733	5.948000	1.1988667	56.51267	18.17267	7.526739	281.1957	2.5736957	0.1458696	Control

knitr::kable(SD, caption = "Table 8. Summary table for the standard deviation (SD) of treated and control plants")

Table 8. Summary table for the standard deviation (SD) of treated and control plants

SD_SL	SD_RL	SD_PH	SD_NL	SD_LA	SD_FW	SD_DW	SD_RWC	SD_Na	SD_K	SD_Ca	SD_Mg	SD_K_NA	SD_EL	SD_CC	SD_PR	SD_I_CO2	SD_TR	SD_SC	Treat_Contr
0.4802925	0.4291682	0.7607550	0.823898	0.0156644	66.00101	3.997402	2.650396	8.841386	7.478913	5.315845	1.827305	0.1959651	9.321449	6.022014	1.017193	42.57340	0.475834	0.0179150	Treatment
0.5639478	0.4196222	0.7721049	0.780235	0.0254147	164.75151	6.887152	6.997688	5.007557	6.091253	2.606707	2.854427	0.3648619	6.479804	4.789543	4.839646	22.89638	1.131070	0.0801618	Control

(3) Pearson correlation

# table with all the variables but without 3 first columns
all_data <- na.omit(table[4:23])

res1 <- cor.mtest(all_data, conf.level = .95)
M1 <-cor(all_data)
corrplot(M1, p.mat = res1$p, sig.level = .05, type = "lower")

Figure 3.1. Pearson correlation table for all variables. X represent non-significative correlation (p-value > 0.05)

(4) Salt tolerance index (STI)

x <- data.frame(dplyr::filter(table, table$Treat_Contr=="Treatment"))
x <- x[-(1:3)]
y <- data.frame(dplyr::filter(table, table$Treat_Contr=="Control"))
y <- y[-(1:3)]


#salt tolerance index for morphological traits
STI_Shoot_Length <- x[1]/y[1]
STI_Root_Length <- x[2]/y[2]
STI_Plant_Height <- x[3]/y[3]
STI_Number_Leaves <- x[4]/y[4]
STI_Leaf_Area <- x[5]/y[5]

#trying to make a for loop
#STI1 <- data.frame()
#vecteur <- c()
#for (i in names(x)){
#  for (j in 1:4){
#    name <- paste("STI", i ,sep = "_" )
#    name <- x[j]/x[j]
#    vecteur <- c(vecteur, name)}}

#salt tolerance index for biomass
STI_Fresh_Weight <- x[6]/y[6]
STI_Dry_Weight <- x[7]/y[7]

#salt tolerance index for RWC, EL and Chlorophyll content
STI_Relative_water_content  <- x[8]/y[8]
names(STI_Relative_water_content)[1] <- "Relative_water_content" # change the name of the column of the data
STI_Electrolyte_Leakage  <- x[14]/y[14]
STI_Chlorophyll_Content <- x[15]/y[15]

# salt tolerance index for Mineral ion content
STI_Na <- x[9]/y[9]
STI_K <- x[10]/y[10]
STI_Ca <- x[11]/y[11]
STI_Mg <- x[12]/y[12]
STI_K_Na <- x[13]/y[13]

# salt tolerance index for Gas exchange attributes
STI_Photosynthesis_rate <- x[16]/y[16]
STI_Intercellular_CO2 <- x[17]/y[17]
STI_Transpiration_Rate <- x[18]/y[18]
STI_Stomatal_Conductance <- x[19]/y[19]

STI <- data.frame(STI_Shoot_Length,
                  STI_Root_Length, 
                  STI_Plant_Height, 
                  STI_Number_Leaves, 
                  STI_Leaf_Area, 
                  STI_Fresh_Weight, 
                  STI_Dry_Weight,
                  STI_Relative_water_content, 
                  STI_Electrolyte_Leakage,
                  STI_Na,
                  STI_K,
                  STI_Ca,
                  STI_Mg,
                  STI_K_Na,
                  STI_Chlorophyll_Content,
                  STI_Photosynthesis_rate,
                  STI_Intercellular_CO2,
                  STI_Transpiration_Rate,
                  STI_Stomatal_Conductance)

knitr::kable(STI, caption = "Table 4.1. Summary table for salt tolerance index")

Table 4.1. Summary table for salt tolerance index

	Shoot_Length	Root_Length	Plant_Height	Number_Leaves	Leaf_Area	Fresh_Weight	Dry_Weight	Relative_water_content	Electrolyte_Leakage	Na	K	Ca	Mg	K_Na	Chlorophyll_Content	Photsynthesis_Rate	Intercellular_CO2	Transpiration_Rate	Stomatal_Conductance
1.1	0.8912355	0.6397671	0.7407407	0.6190476	0.6956522	0.5012338	0.8625796	1.1842036	0.9481757	4.673913	1.3245413	0.8746888	0.9281364	0.2824561	0.8084896	0.3725332	0.8583916	0.5174051	0.3563218
2.1	0.5455830	0.6594203	0.5068109	0.6363636	0.5000000	0.3398344	0.7523677	1.2812980	1.0102382	3.953065	1.4744608	0.8739300	0.8126195	0.3735763	0.7181102	0.6496350	1.0626151	1.2375691	1.0000000
3.1	0.1974957	0.1920136	0.2569204	0.3027273	0.0759494	0.1019714	0.2466527	1.1603017	1.2178019	2.284498	1.5078206	1.0076112	0.9375000	0.6530612	0.7071429	0.0788235	0.8881239	0.1327751	0.0720000
4.1	0.5805727	0.7180233	0.6326396	0.8260870	0.4375000	0.4774903	0.6312320	1.0031933	1.0765362	3.128941	1.4041326	1.0880893	0.7604439	0.4489796	0.9398422	0.2615385	0.9784946	0.3887324	0.2941176
5.1	0.4346774	0.5895884	0.4631766	0.4545455	0.8484848	0.7639138	0.8376289	1.0355254	1.3243824	2.762542	1.0798784	0.7580340	0.6751035	0.3922652	0.7092059	0.2883031	1.1160714	0.5616836	0.2592593
6.1	0.6141575	0.7054054	0.6148649	0.8260870	0.3750000	0.3125481	0.3173846	0.9711378	0.9810355	2.349392	1.2147568	0.9583333	0.7517107	0.5198238	0.7365854	0.2469758	1.1190053	0.2654639	0.1604938
7.1	0.7641026	0.5242452	0.6591577	0.6000000	0.7368421	0.2985661	0.8040881	0.9847842	1.0683937	2.336629	1.0196235	0.7644593	0.7346570	0.4330709	1.1907478	NA	NA	NA	NA
8.1	0.3086253	0.5849057	0.4244635	0.4800000	0.6285714	0.3107289	0.4988095	0.9729330	1.1970276	2.046184	1.2483425	0.8205567	0.9322093	0.6106195	0.7074830	0.5376532	1.1081531	0.8280255	0.7777778
9.1	0.7061258	0.6245520	0.6665232	0.7500000	0.6000000	0.8392562	0.7816092	0.9858376	1.1139822	1.964433	1.2366031	1.4981454	0.6565325	0.6274194	1.1429440	0.1755889	0.9843137	0.1965318	0.1428571
10.1	0.7718023	0.4665012	0.6230263	0.4400000	0.8536585	0.4080294	0.4489583	0.9825059	1.0069524	3.665535	1.3353756	1.8146307	0.8441433	0.3564103	0.7020702	0.2857955	0.9182948	0.3548851	0.3623188
11.1	0.6143411	0.5955649	0.5832497	0.8181818	0.8214286	0.7029466	0.8826356	1.0131178	0.9996619	1.171353	1.1260306	1.1241696	0.9644747	0.9585122	0.6530850	0.3531202	1.1344828	0.3829787	0.3703704
12.1	0.7705287	0.7352587	0.6868100	0.6818182	0.4000000	0.7821395	0.8580247	1.0048024	1.0193132	1.220186	0.8205479	1.7769486	0.9071938	0.6742857	0.9170213	0.3651407	1.1017613	0.5875796	0.4262295
13.1	0.7205560	0.5748599	0.6379633	0.8000000	0.5945946	0.3933007	0.8412844	0.9843384	1.0147048	2.641426	1.0343169	1.4672783	0.7078189	0.3921569	0.6055556	0.1689189	0.7602041	0.1854396	0.1234568
14.1	0.5388646	0.6171096	0.5854576	0.5714286	0.3571429	0.3886307	0.6150000	0.9679176	1.1095468	2.769071	1.4512663	2.2868976	0.8902148	0.5228758	0.7039518	0.3601359	0.9320883	0.4835590	0.2592593
15.1	0.1673114	0.4004955	0.3745438	0.3368421	0.1851852	0.1458749	0.3050847	0.9623137	1.0466700	1.361111	1.3379447	1.3552941	0.6887417	0.9751553	0.5820745	0.2655039	0.6830428	0.1347150	0.0952381
16.1	0.2872517	0.1958406	0.2637694	0.2895652	0.1714286	0.4290074	0.8201635	0.9788952	1.0586022	2.142991	1.4204156	2.2103825	0.7299742	0.6483376	0.5970149	NA	NA	NA	NA
17.1	0.2446634	0.2021563	0.2612958	0.2800000	0.2894737	0.1725975	0.2884615	0.9644903	1.0599136	2.632761	1.5583658	3.1660542	0.7332268	0.6041667	0.6070252	0.6666667	0.9264706	0.3474178	0.5263158
18.1	0.1956664	0.1590909	0.1982964	0.3846154	0.1621622	0.1332756	0.3225000	0.9176678	1.0951118	1.540048	1.1216572	2.7861528	0.7603960	0.7338129	1.2998267	0.5008787	1.1421569	0.7168459	0.3703704
19.1	0.5484444	0.6816514	0.6327913	0.9411765	0.7142857	0.6726554	0.7635853	1.0186102	1.0286753	2.615610	1.2084392	2.6221228	0.6307008	0.4625850	0.8808889	0.7505774	1.0209059	0.1552198	0.1851852
20.1	0.1738693	0.4141759	0.6471132	0.5333333	0.3157895	0.7136095	0.8387097	1.0340028	1.0453706	3.529240	1.2608368	1.6038576	0.6960352	0.3685567	0.6601650	0.1377953	0.9054622	0.1533333	0.1230769
21.1	0.2930187	0.1722595	0.3778626	0.6250000	0.4500000	0.6796849	0.7768924	1.0045268	1.0491827	1.803975	1.2245062	1.4547284	0.5526565	0.6750000	1.6984925	0.2113254	0.9689781	0.2971014	0.2323232
22.1	0.6120603	0.3154907	0.4909931	0.5882353	0.8571429	0.7436083	0.6534979	0.9949697	1.1884034	1.687400	1.2295189	1.4203516	0.8420735	0.7324561	0.4223003	0.2919109	1.0617761	0.6226415	0.5217391
23.1	0.3644752	0.2955556	0.3299377	0.6666667	0.6400000	0.2726450	0.8100000	0.9623571	1.0422465	1.544219	1.5155393	1.1520648	0.7255521	0.9841270	0.4108527	0.1536174	1.0731707	0.4244373	0.4000000
24.1	0.5350501	0.3480114	0.4440535	0.5333333	0.8000000	0.6160173	0.4104348	0.9850878	1.1019697	2.048939	1.3736817	1.1574557	0.3929825	0.6721854	0.6623681	0.2011173	1.3056058	0.4013158	0.4347826
25.1	0.4878661	0.2348651	0.3546893	0.6666667	1.0000000	0.4188665	0.8152585	0.9636486	0.9610544	2.716135	1.8820856	2.0229358	0.8764309	0.6875000	0.6529412	0.3127753	1.3176692	0.3857868	0.4800000

(5) Membership function value (MFV)

#Membership function value 
# 1-results for the traits inversely related to salt tolerance: which are electrolyte leakage, Na, K and Ca
MFV <- STI %>%
  mutate(SL = (STI$Shoot_Length - min(STI$Shoot_Length))/(max(STI$Shoot_Length)- min(STI$Shoot_Length)),
         RL = (STI$Root_Length - min(STI$Root_Length))/(max(STI$Root_Length)- min(STI$Root_Length)),
         PH = (STI$Plant_Height - min(STI$Plant_Height))/(max(STI$Plant_Height)- min(STI$Plant_Height)),
         NL = (STI$Number_Leaves - min(STI$Number_Leaves))/(max(STI$Number_Leaves)- min(STI$Number_Leaves)),
         LA = (STI$Leaf_Area - min(STI$Leaf_Area))/(max(STI$Leaf_Area)- min(STI$Leaf_Area)),
         FW = (STI$Fresh_Weight - min(STI$Fresh_Weight))/(max(STI$Fresh_Weight)- min(STI$Fresh_Weight)) ,
         DW = (STI$Dry_Weight - min(STI$Dry_Weight))/(max(STI$Dry_Weight)- min(STI$Dry_Weight)),
         RWC = (STI$Relative_water_content - min(STI$Relative_water_content))/(max(STI$Relative_water_content)- min(STI$Relative_water_content)),
         EL = 1-(STI$Electrolyte_Leakage - min(STI$Electrolyte_Leakage))/(max(STI$Electrolyte_Leakage)- min(STI$Electrolyte_Leakage)),
         CC = (STI$Chlorophyll_Content - min(STI$Chlorophyll_Content))/(max(STI$Chlorophyll_Content)- min(STI$Chlorophyll_Content)),
         Na = 1-(STI$Na - min(STI$Na))/(max(STI$Na)- min(STI$Na)),
         K = 1-(STI$K - min(STI$K))/(max(STI$K)- min(STI$K)),
         Ca =1- (STI$Ca - min(STI$Ca))/(max(STI$Ca)- min(STI$Ca)),
         Mg=(STI$Mg - min(STI$Mg))/(max(STI$Mg)- min(STI$Mg)),
         K_Na= (STI$K_Na - min(STI$K_Na))/(max(STI$K_Na)- min(STI$K_Na)),
         PR= (STI$Photsynthesis_Rate - min(STI$Photsynthesis_Rate, na.rm=T))/(max(STI$Photsynthesis_Rate, na.rm=T)- min(STI$Photsynthesis_Rate, na.rm=T)),
         ICO2= (STI$Intercellular_CO2 - min(STI$Intercellular_CO2, na.rm=T))/(max(STI$Intercellular_CO2, na.rm=T)- min(STI$Intercellular_CO2, na.rm=T)),
         TR= (STI$Transpiration_Rate - min(STI$Transpiration_Rate, na.rm=T))/(max(STI$Transpiration_Rate, na.rm=T)- min(STI$Transpiration_Rate, na.rm=T)),
         SC= (STI$Stomatal_Conductance - min(STI$Stomatal_Conductance, na.rm=T))/(max(STI$Stomatal_Conductance, na.rm=T)- min(STI$Stomatal_Conductance, na.rm=T)))%>%
  select(SL, RL, PH, NL, LA, FW, DW, RWC, EL, CC, Na, K, Ca, Mg, K_Na, PR, ICO2, TR, SC)

knitr::kable(MFV, caption = "Table 5.1. MFV table based on STI", align="c")

Table 5.1. MFV table based on STI

SL	RL	PH	NL	LA	FW	DW	RWC	EL	CC	Na	K	Ca	Mg	K_Na	PR	ICO2	TR	SC
1.0000000	0.8342641	1.0000000	0.5127944	0.6706373	0.5415308	0.9684647	0.7329858	1.0000000	0.3088107	0.0000000	0.5252233	0.9515557	0.9364151	0.0000000	0.4372282	0.2763024	0.3481463	0.3063813
0.5225294	0.8683744	0.5687487	0.5389841	0.4589041	0.3226203	0.7951708	1.0000000	0.8350309	0.2386207	0.2058062	0.3839946	0.9518709	0.7342830	0.1298617	0.8497332	0.5981036	1.0000000	1.0000000
0.0416954	0.0571408	0.1080738	0.0343740	0.0000000	0.0000000	0.0000000	0.6672545	0.2833031	0.2301033	0.6821910	0.3525687	0.8963558	0.9527996	0.5281751	0.0000000	0.3231526	0.0000000	0.0000000
0.5708628	0.9700860	0.8007147	0.8259322	0.3912671	0.5093267	0.6047008	0.2351993	0.6588033	0.4108210	0.4410979	0.4502459	0.8629350	0.6429858	0.2373242	0.2719969	0.4655524	0.2316787	0.2393509
0.3693288	0.7471738	0.4883085	0.2639922	0.8360315	0.8978110	0.9292329	0.3241139	0.0000000	0.2317055	0.5457070	0.7557030	1.0000000	0.4936569	0.1564965	0.3118398	0.6823363	0.3882249	0.2017880
0.6172555	0.9481864	0.7679469	0.8259322	0.3236301	0.2856111	0.1112167	0.1470450	0.9126550	0.2529688	0.6636635	0.6286436	0.9168199	0.6277044	0.3382892	0.2503183	0.6869594	0.1201028	0.0953597
0.8243836	0.6337638	0.8496011	0.4839858	0.7152127	0.2666469	0.8764943	0.1845734	0.6804469	0.6056780	0.6673075	0.8124649	0.9973317	0.5978639	0.2146515	NA	NA	NA	NA
0.1952055	0.7390464	0.4169407	0.3024911	0.5980431	0.2831436	0.3964837	0.1519818	0.3385234	0.2303674	0.7502312	0.5970048	0.9740356	0.9435420	0.4676884	0.6830325	0.6698591	0.6293032	0.7605364
0.7442969	0.8078568	0.8631795	0.7108541	0.5671233	1.0000000	0.8411492	0.1874700	0.5592676	0.5685529	0.7735715	0.6080637	0.6926474	0.4611611	0.4916311	0.1440488	0.4747217	0.0577091	0.0763547
0.8350198	0.5335430	0.7829926	0.2419929	0.8416305	0.4151151	0.3180992	0.1783078	0.8437650	0.2261638	0.2878975	0.5150171	0.5612177	0.7894435	0.1053972	0.3081068	0.3706938	0.2010420	0.3128436
0.6175091	0.7575467	0.7096642	0.8139761	0.8067515	0.8151195	1.0000000	0.2624920	0.8631439	0.1881211	1.0000000	0.7122262	0.8479516	1.0000000	0.9634945	0.4083292	0.7113476	0.2264708	0.3215198
0.8332604	1.0000000	0.9005783	0.6077321	0.3506849	0.9225310	0.9613026	0.2396242	0.8109084	0.3930980	0.9860579	1.0000000	0.5768663	0.8997697	0.5584236	0.4262233	0.6597874	0.4116645	0.3817128
0.7642301	0.7216109	0.8105291	0.7864769	0.5612736	0.3951381	0.9349807	0.1833474	0.8231580	0.1512091	0.5802862	0.7986233	0.7054658	0.5509025	0.1563421	0.1341196	0.1215854	0.0476690	0.0554491
0.5132489	0.7949398	0.7137344	0.4407728	0.3043053	0.3888040	0.5791780	0.1381892	0.5710574	0.2276250	0.5438428	0.4058446	0.3650952	0.8700597	0.3426388	0.4187730	0.3924285	0.3175107	0.2017880
0.0000000	0.4189831	0.3249133	0.0859712	0.1182141	0.0595475	0.0918767	0.1227783	0.7381909	0.1329734	0.9458231	0.5125969	0.7519705	0.5175211	0.9872138	0.2778999	0.0000000	0.0017559	0.0250411
0.1656807	0.0637829	0.1206998	0.0144670	0.1033268	0.4435681	0.9017708	0.1683783	0.7064738	0.1445763	0.7225921	0.4349068	0.3968703	0.5896698	0.5214432	NA	NA	NA	NA
0.1068509	0.0747446	0.1161398	0.0000000	0.2310743	0.0957922	0.0657389	0.1287642	0.7029881	0.1523505	0.5827600	0.3049537	0.0000000	0.5953613	0.4584921	0.8750871	0.3835766	0.1942830	0.4895644
0.0391685	0.0000000	0.0000000	0.1582261	0.0932988	0.0424588	0.1192599	0.0000000	0.6094273	0.6903903	0.8947355	0.7163462	0.1577650	0.6429022	0.6432600	0.6282885	0.7234399	0.5286694	0.3215198
0.5264821	0.9069588	0.8009945	1.0000000	0.6908023	0.7740346	0.8128090	0.2775962	0.7860230	0.3650370	0.5876568	0.6345949	0.2258833	0.4159607	0.2567142	1.0000000	0.5323812	0.0203157	0.1219668
0.0090589	0.4427269	0.8273969	0.3831554	0.2595530	0.8295818	0.9309323	0.3199268	0.7416450	0.1936196	0.3268105	0.5852348	0.6487473	0.5302833	0.1227079	0.0877877	0.3504730	0.0186082	0.0550398
0.1736470	0.0228555	0.3310315	0.5217972	0.4047945	0.7835690	0.8337327	0.2388663	0.7315120	1.0000000	0.8193829	0.6194593	0.7106775	0.2793985	0.5594416	0.1972477	0.4505569	0.1487393	0.1727621
0.6143585	0.2714483	0.5395884	0.4661922	0.8454012	0.8702701	0.6397110	0.2125838	0.3614476	0.0088903	0.8526658	0.6147372	0.7249535	0.7858219	0.6413263	0.3172105	0.5967815	0.4434007	0.4846327
0.2723542	0.2368488	0.2426817	0.5848161	0.6104110	0.2314894	0.8857901	0.1228976	0.7499491	0.0000000	0.8935448	0.3452975	0.8363673	0.5819320	1.0000000	0.1113413	0.6147364	0.2639969	0.3534483
0.5079796	0.3278914	0.4530549	0.3831554	0.7835616	0.6972148	0.2575259	0.1854081	0.5911980	0.1953306	0.7494444	0.4789316	0.8341286	0.0000000	0.5554304	0.1820515	0.9809914	0.2430686	0.3909295
0.4428015	0.1315140	0.2883114	0.5848161	1.0000000	0.4298137	0.8940584	0.1264494	0.9657671	0.1880095	0.5589565	0.0000000	0.4747130	0.8459405	0.5772562	0.3482701	1.0000000	0.2290126	0.4396552

(6) Principal component analysis (PCA)

####------PCA on MFV-------###

#doing the PCA, adding the scaling into the function
MFV_salt.pca <- prcomp((na.omit(MFV[-c(8, 17, 20)])), scale. = TRUE)
#results of the PCA
MFV_salt.pca$sdev

##  [1] 2.1959249 1.6698449 1.3565852 1.2856275 1.2158618 1.1326328 0.9173147
##  [8] 0.8378035 0.7115879 0.6292612 0.4811136 0.4495871 0.3451879 0.2533067
## [15] 0.2128659 0.1474844 0.0741877

summary(MFV_salt.pca)

## Importance of components:
##                           PC1    PC2    PC3     PC4     PC5     PC6    PC7
## Standard deviation     2.1959 1.6698 1.3566 1.28563 1.21586 1.13263 0.9173
## Proportion of Variance 0.2837 0.1640 0.1082 0.09723 0.08696 0.07546 0.0495
## Cumulative Proportion  0.2837 0.4477 0.5559 0.65315 0.74011 0.81558 0.8651
##                            PC8     PC9    PC10    PC11    PC12    PC13    PC14
## Standard deviation     0.83780 0.71159 0.62926 0.48111 0.44959 0.34519 0.25331
## Proportion of Variance 0.04129 0.02979 0.02329 0.01362 0.01189 0.00701 0.00377
## Cumulative Proportion  0.90636 0.93615 0.95944 0.97306 0.98495 0.99196 0.99573
##                           PC15    PC16    PC17
## Standard deviation     0.21287 0.14748 0.07419
## Proportion of Variance 0.00267 0.00128 0.00032
## Cumulative Proportion  0.99840 0.99968 1.00000

#check, if the sums equal to quantity of observations (=17)
sum((MFV_salt.pca$sdev)^2)

## [1] 17

#plotting the results in order to evaluate the principal components
MFV_plot <- screeplot(MFV_salt.pca,
                      npcs = length(MFV_salt.pca$sdev),
                      type = "lines")

#plotting the results of the PCA
library(ggplot2)
#install.packages("ggfortify")
library(ggfortify)
#making a scatterplot of PC1 and PC2
#creating data.frame for data labels in future plot
MFV_labels <- data.frame(Acc_loc$Elevation[-c(7, 16)])
row.names(MFV_labels) <- paste0("Ess-", c(1:6, 8:15, 17:25))
#creating the plot
MFV_pc12 <- autoplot(MFV_salt.pca, loadings = TRUE, loadings.colour = 'blue',
         loadings.label = TRUE, loadings.label.size = 5,                #plotting the variables
         data = MFV_labels, shape = FALSE, label.size = 3,              #plotting the accessions
         frame = TRUE)                                                  #plotting the frame (to see if there are any groups)
MFV_pc12 + ggtitle("Scatterplot of PC1 and PC2")                            #adding the title

#putting absolute values of the loading scores from PCA1 into a vector
MFV_loading_scores <- abs(MFV_salt.pca$rotation[,1])
#ranking the variables
MFV_var_scores_ranked <- sort(MFV_loading_scores, decreasing = TRUE)
# We used 8 highest ranked variables to include in the final MFV-based ranking
# of salt tolerance accessions
print(MFV_var_scores_ranked) # Result from PCA analysis (from PCA_analysis.R)

##           PH           SL           RL           NL           DW           FW 
## 0.4179220239 0.3843371322 0.3655251106 0.3440154296 0.3304446450 0.3118996483 
##           LA         K_Na           Na            K           Ca           EL 
## 0.2467265352 0.2306101451 0.1746912100 0.1736000724 0.1438780842 0.1395916180 
##           Mg           TR           SC           PR           CC 
## 0.0455479807 0.0433627723 0.0156462600 0.0100021446 0.0005731188

(7) MFV Ranking

MFV <- MFV %>%
  mutate(Mean = rowMeans(MFV[c(1:7, 15)], na.rm=T))%>%
  select(SL, RL, PH, NL, LA, FW, DW, RWC, EL, CC, Na, K, Ca, Mg, K_Na, PR, ICO2, TR, SC, Mean)

MFV_Ranked <- MFV[order(-MFV$Mean),]
MFV_Ranked <- MFV_Ranked[20]
MFV_Ranked$Category <- c("Highly tolerant",(rep("Tolerant", 5)), (rep("Moderately tolerant", 13)), (rep("Sensitive", 5)), "Highly sensitive")

knitr::kable(MFV_Ranked, caption = "Table 7.1. Salt tolerance ranking based on MFV mean from 8 variables identified by PCA", align="c")

Table 7.1. Salt tolerance ranking based on MFV mean from 8 variables identified by PCA

	Mean	Category
11	0.8105077	Highly tolerant
12	0.7668141	Tolerant
9	0.7532614	Tolerant
19	0.7210994	Tolerant
1	0.6909614	Tolerant
13	0.6413227	Tolerant
4	0.6137768	Moderately tolerant
22	0.6110370	Moderately tolerant
7	0.6080925	Moderately tolerant
5	0.5860469	Moderately tolerant
25	0.5435714	Moderately tolerant
6	0.5272585	Moderately tolerant
2	0.5256492	Moderately tolerant
14	0.5097027	Moderately tolerant
10	0.5092238	Moderately tolerant
23	0.5080489	Moderately tolerant
24	0.4957268	Moderately tolerant
20	0.4756391	Moderately tolerant
21	0.4538586	Moderately tolerant
8	0.4248803	Sensitive
16	0.2918424	Sensitive
15	0.2608400	Sensitive
17	0.1436041	Sensitive
18	0.1369590	Sensitive
3	0.0961824	Highly sensitive

(8) Dendogram

#table with MFV from 8 important variables of PCA
important_var <- na.omit(MFV)
important_var <- MFV[c(1:7, 15)]

#make the dendogram
dend <- important_var %>% # change MFV_Ranked with important_var in function of professor answer 
  scale %>% 
  dist(method = "euclidean") %>% 
  hclust(method = "ward.D2") %>% 
  as.dendrogram %>%
  set("branches_k_color", 
      value = c("red", "orange","blue", "green"), 
      k = 4)

#plot dendogram
dend %>% plot(main = "MFV Dendrogram")

Figure 8.1. The MFV dendogram based on 8 variables identified by PCA

(9) Accessions map

Discussion

From your objectives.

The priliminary results, despite being slightly different from that in the article produced the ranking that corresponds to the groups (Highly tolerant - Highly sensitive) outlined in the article.

Conclusions

The analysis is not yet completed, so conclusions will be drawn later.